Ao Yin, Xingyu Fu, Xinxin Liu, Min Li, Xiaochen Yu, Xiuru Guan
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引用次数: 0
Abstract
Background: Autophagy exerts a vital role in the development of atherosclerotic lesions. Mounting evidence suggests a significant link between autophagy and atherosclerosis.
Methods: Two atherosclerotic plaque datasets were integrated from the Gene Expression Omnibus (GEO) database. After differentially expressed genes (DEGs) were determined, enrichment analyses were subsequently performed on DEGs. We employed weighted gene coexpression network analysis (WGCNA) and cross-linked these modules with DEGs and autophagy-related genes. Subsequently, a prediction model was established for evaluation. RT-PCR was adopted to identify hub gene expression. The consensus clustering analysis on the overlapping genes was executed. Evaluation of immune infiltration was conducted on the merged dataset. A TF-miRNA-mRNA regulatory network was then established for the hub genes.
Results: The differential gene expression analysis uncovered 259 DEGs. Enrichment analysis showed that immune and inflammatory reactions were related to atherosclerosis. By intersecting DEGs, WGCNA module genes, and ARGs, 13 overlapping genes were obtained. Four machine learning models identified seven hub genes. Furthermore, six of the seven genes demonstrated potential for disease diagnosis. The prediction model, based on the expression levels of these six genes, yielded satisfactory results. RT-PCR analysis demonstrated that the mRNA expression of six genes meets expectations. Consensus clustering divides 13 overlapping genes into two clusters, C1 and C2, with significant differences in immune infiltration. Immune cell infiltration demonstrated heightened immune activity within the atherosclerotic plaque group. A TF-miRNA-mRNA regulatory network was established for the six genes.
Conclusion: It is anticipated that these six genes may serve as significant and valuable targets for future research into atherosclerosis.
背景:自噬在动脉粥样硬化病变的发展中起着至关重要的作用。越来越多的证据表明,自噬与动脉粥样硬化之间存在重要联系。方法:从Gene Expression Omnibus (GEO)数据库中整合两个动脉粥样硬化斑块数据集。在确定差异表达基因(deg)后,随后对deg进行富集分析。我们采用加权基因共表达网络分析(WGCNA),并将这些模块与deg和自噬相关基因交联。随后,建立预测模型进行评价。采用RT-PCR方法鉴定枢纽基因表达。对重叠基因进行一致聚类分析。对合并后的数据集进行免疫浸润评价。然后建立了枢纽基因的TF-miRNA-mRNA调控网络。结果:差异基因表达分析发现259个DEGs。富集分析表明免疫和炎症反应与动脉粥样硬化有关。将deg、WGCNA模块基因和arg交叉,得到13个重叠基因。四个机器学习模型确定了七个中心基因。此外,七个基因中的六个显示出疾病诊断的潜力。基于这6个基因表达水平的预测模型取得了令人满意的结果。RT-PCR分析显示6个基因的mRNA表达符合预期。共识聚类将13个重叠基因分为C1和C2两个簇,免疫浸润差异显著。免疫细胞浸润显示动脉粥样硬化斑块组免疫活性增强。建立了6个基因的TF-miRNA-mRNA调控网络。结论:这6个基因有望成为未来动脉粥样硬化研究的重要靶点。